The impact of Machine Learning in BFSI in India

The impact of Machine Learning in BFSI in India

The impact of Machine learning in BFSI (Banking, Financial Services and insurance) domain is great due to ML-based algorithms that are capable of predicting more precise results when fed with more data.


The remarkable growth in digital technologies like Robotic Process Automation (RPA), Machine Learning, Artificial Intelligence, Blockchain has given rise to the evolution of the 4.0 Industrial Revolution. In history, machines have never been able to match human intelligence with such ease.There is no doubt that within a business, ML is disrupting mostly all the functions. Coincidentally, in BFSI sectors, there are petabytes of data on Customers, invoices, transactions, money transfers, etc. which helps these algorithm-based models to learn and improve continuously.

According to the latest forecast by Gartner’s CIO, AI and Machine Learning is the number one game-changing technology in banking and securities sectors in India. BFSI continues to increase its investment in Digital Business, and its spending in India will total $11 billion in 2020, as an increase of 9.1% compared to 2019.

Besides some primitive applications of ML in BFSI like applications embedded in end-user devices or designing voice-assisted banking processes, personal robots, applications embedded in end-user devices, Financial institutions’ servers can provide customized financial advice, calculations, and forecast, and analyzing massive volumes of information. Such applications help in developing financial strategies and track their progress.

The Solution offered by ML in the banking domain is a faster, more accurate, and cost-effective decision-making system for credit scoring and lending. As compared to the traditional credit scoring model based on pre-defined rules, ML provides a more complex and sophisticated system to help bankers distinguish between high-level risk applicants from more credit-worthy ones.In the Insurance sectors, ML helps to review the customer’s profiles and provide personalized solutions and specific products suitable as per the customer’s needs. Also, ML can be applied for processing thousands of claims and responding to various customer’s queries daily.ML enhances these processes by streamlining the movement of claim-related reports from initial application to final decision instantly.In the BFSI sector, ML models help in not only identifying fraudulent claims but also highlighting it for further investigation and offers an explicit decision-making method to avoid human bias.


Many Companies use ML-based solutions to optimize the activity level of customers and also reward customers based on their good behavior by offering them discounts. With such a comprehensive consumer view, Insurance companies can better manage risk too. However, as of date, just thirty percent of the companies have implemented ML-based solutions into their working processes. Most of the companies are concerned about expenses, time, and efforts for implementing these solutions in financial services versus the expected ROI. Currently, the major challenge is that one can’t easily deny the importance of ML-based solutions, and the more companies delay in adopting today may cost heavily to them in the long run.